#transportability
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prepub-singlecell.bsky.social
Representation-dependent transportability of single-cell-derived myeloid- and fibroblast/stromal-associated transcriptional programs in bladder cancer #SingleCell 🧪🧬🖥️
https://www.researchsquare.com/article/rs-11050428/latest
November 2, 2026 at 11:00 PM
cslg-bot.bsky.social
Benjamin T. Pike: Physiological Trajectories and Observation Patterns in ICU Mortality Prediction: Transportability Under Changing Measurement Conditions https://arxiv.org/abs/2610.42126 https://arxiv.org/pdf/2610.42126 https://arxiv.org/html/2610.42126
November 2, 2026 at 6:44 AM
atpablonaruto.bsky.social
From a culinary perspective, it has the same properties as egg whites, so you could make it into meringues - even if some potency is lost in the processing, you'd gain in shelf-stablility and transportability. Plus, you could add herbal components for additional health benefits.
October 23, 2026 at 3:54 PM
prepub-neurodegen.bsky.social
Cross-Cohort Transportability of Multi-Family Resting-State EEG Microstate Dynamics in Parkinson's Disease #NeuroDegeneration 🧪🧠
https://www.researchsquare.com/article/rs-11136934/latest
October 22, 2026 at 4:03 PM
davidmogeladze.bsky.social
bioengineer.org/explainable-...
Explainable machine learning reveals limits in nanoparticle tumor-delivery
A leakage-aware evaluation of explainable machine learning found modest prediction of nanoparticle tumor delivery, with errors increasing for unseen materials and little transportability across
bioengineer.org
October 20, 2026 at 3:40 PM
cannoncloud.bsky.social
Gotta nail the DGP for transportability.
October 17, 2026 at 7:41 AM
cannoncloud.bsky.social
Im working on a project where i am aiming for the DGP through sampling design and variable choice. Grid searching over these hyper parameters and then verifying transportability in new spatial/temporal domains. Seems like there is massive room for improvement in this middle route.
October 17, 2026 at 8:10 AM
haivian-journal.bsky.social
Two clocks derived from 18,476 participants can separate chronological age from systemic risk, but transportability is the real test. External cohorts should probe calibration across ancestry, diet, medication and assay platforms, with prospective endpoints rather than cross-sectional fit alone.
October 16, 2026 at 6:07 PM
statsepi.bsky.social
Dont forget observational studies with external transportability problems.
October 10, 2026 at 3:46 PM
hapsis.bsky.social
Lockheed Martin HIMARS FLEX Doubles Firepower and Expands Into Air and Missile Defense

www.armyrecognition.com/news/army-ne...

#MilitaryTech #Defense #SituationalAwareness
Lockheed Martin HIMARS FLEX Doubles Firepower and Expands Into Air and Missile Defense
Lockheed Martin's HIMARS FLEX doubles missile capacity, adds PAC-3 air defense options and retains C-130 transportability.
www.armyrecognition.com
October 12, 2026 at 10:21 AM
jwbelmon.bsky.social
Untestable exchangeability assumptions

[oops, sorry, that’s rcts with external transportability problems]
statsepi.bsky.social Darren Dahly @statsepi.bsky.social · 28d
Many papers I see:

A global problem
Search for modifiable "risk factors"
A cohort study sorry I mean real-world data
"Multivariate" regression
Some associations
Can't say cause
Residual confounding
Policy must change in ways having nothing to do with our findings
More research is needed
October 10, 2026 at 2:30 PM
tastingtable.bsky.social
The steinie (or stubby) beer bottle was created to compete with the transportability and availability of the beer can, becoming iconic in American culture.
The Beer Bottle That Looks Like It Should Have A '70s Mustache - Tasting Table
www.tastingtable.com
October 10, 2026 at 11:37 AM
mauriceoconnell.bsky.social
Great work by my PhD student @manchester.ac.uk , Kritchavat Ploddi, on this publication! 👏, and thanks to my fellow supervisors @mattsperrin.bsky.social & @glenmartin.bsky.social

📄 doi.org/10.1186/s415...

#ClinicalPredictionModels #Generalisability #Transportability #DataShift #CausalInference
October 9, 2026 at 9:38 PM
tastingtable.bsky.social
The steinie (or stubby) beer bottle was created to compete with the transportability and availability of the beer can, becoming iconic in American culture.
The Beer Bottle That Looks Like It Should Have A '70s Mustache - Tasting Table
www.tastingtable.com
October 7, 2026 at 9:32 PM
paperposterbot.bsky.social
arXiv📈🤖
Data Fusion for Errors-in-Variables
By Zhao, Liu, Wang
We study errors-in-variables problems in which a target study contains only a single error-prone surrogate of an unobserved exposure, while an external source study provides repeated surrogate measurements from a different population. The measurement error distribution is allowed to depend on the observed error-free variables, and the error-free variable distribution itself may differ between studies. We introduce a conditional transportability assumption that enables the use of external repeated measurements under source-target heterogeneity. Together with additional replicate-error conditions, it identifies the target conditional measurement-error distribution. Building on this identification result, we develop a data-fusion estimator for a broad class of target functionals. The estimator combines conditional deconvolution, flexible nuisance estimation, and orthogonal correction that reduces first-order sensitivity to nuisance estimation. For the proposed estimator, we develop a unified spectral theory covering both diffuse-spectrum and finite atomic-spectrum target functionals, derive a general asymptotic expansion, and establish consistency and target-specific convergence-rate bounds. The resulting convergence-rate bounds depend jointly on the spectral properties of the measurement error, the latent exposure, and the target functional. For finite atomic-spectrum targets, we further establish joint Gaussian and bootstrap limits, yielding inference for smooth moment transformations under an additional centering condition. In the reported simulations, Fuse-EIV has small bias for the primary exposure-related coefficient. Applications to the National Health and Nutrition Examination Survey illustrate how accounting for population heterogeneity and error heteroscedasticity can change empirical conclusions.
October 7, 2026 at 11:40 AM
stijn-masschelein.net
What I am hearing is that LLMs should only be used in controlled environments after approval by a university ethics committee. #transportability #external-validity #boundary-conditions
matthodges.bsky.social Matt Hodges @matthodges.bsky.social · Oct 3
There's growing experimental evidence¹˒²˒³˒⁴ that LLMs are unusually good at talking people out of entrenched false beliefs and conspiracy theories.

¹ pubmed.ncbi.nlm.nih.gov/39264999/
² pubmed.ncbi.nlm.nih.gov/41179846/
³ sciety.org/articles/act...
⁴ connecticut.adl.org/resources/re...
October 4, 2026 at 7:42 AM
statme-bot.bsky.social
Sarah E Robertson, Lan Wen, Lawson Ung, Jon A Steingrimsson, Issa J Dahabreh: Transportability methods for failure-time outcomes under assumptions for relative effect measures https://arxiv.org/abs/2610.02041 https://arxiv.org/pdf/2610.02041 https://arxiv.org/html/2610.02041
October 2, 2026 at 7:03 AM
paperposterbot.bsky.social
arXiv📈🤖
Transportability methods for failure-time outcomes under assumptions for relative effect measures
By Robertson, Wen, Ung et al
In certain clinical areas, relative effect measures are believed to remain more constant across populations than absolute measures. This suggests that exchangeability assumptions on the relative scale may be more plausible than those on the absolute scale. Despite this, most methods for extending trial results to a target population rely on strong and often implausible distributional exchangeability assumptions between the trial and target population (distributional transportability). We propose identification results for failure-time outcomes under a more plausible assumption of exchangeability on the relative scale, focusing primarily on the risk ratio (risk ratio transportability). We applied these methods to estimate the effect of screening strategies on all-cause mortality, using data from the National Lung Screening Trial (NLST) to a nationally representative target population from the National Health Interview Survey (NHIS). All-cause mortality data was available from NHIS, along with no confounding of screening, allowing a falsification assessment of the distributional transportability analysis against the observed baseline risk.We found that this approach substantially underestimated mortality risk. In comparison, the risk ratio transportability approach produced reasonable estimates that matched the baseline risk.Thus, assumptions on relative scales may be more plausible in certain settings where distributional transportability fails.
October 2, 2026 at 11:03 AM
paperposterbot.bsky.social
arXiv📈🤖
Bridge-Anchored Partial Identification of a Target Mean under Outcome-Model Drift
By Lee, Jeon, Yang
Transporting an outcome relationship from a source population to a target population where the outcome is unobserved requires the conditional outcome law to be stable across populations. When it is not (outcome-model drift), the target mean is not point-identified and existing transportability estimators are biased. We study settings, common in cross-cohort educational and biomedical data integration, in which bridge outcomes are recorded in both populations and can be expected to drift alongside the target outcome. The bridge splits the drift into a component it can detect, fixed by a bridge-matching equation, and a residual orthogonal to the bridge that no observed quantity restricts. Because the target is a mean, the residual acts through a single scalar sensitivity parameter $\kappa$. We prove that the identified set has a closed-form irreducible core, whose width is governed by the residual bound and by how much of the outcome the bridge leaves unexplained, and that its center is first-order invariant to the residual. These results hold for any working exponential family, with point identification arising only as the $\kappa=0$ benchmark. We develop a debiased, drift-augmented estimator that is semiparametrically efficient at anchored sensitivity, doubly robust conditional on the bridge-identified drift, and yields rate-robust Imbens-Manski inference for the set. Unlike a bridge-blind sensitivity analysis, which must bound the entire drift channel, the proposed analysis bounds only its bridge-orthogonal part. Simulations confirm that the drift estimator is unbiased under co-drift, that the core width follows its closed form, and that the set fails visibly once the residual bound is exceeded. The motivating setting is the integration of two survey cohorts whose kindergarten mathematics outcome is available in only one.
October 1, 2026 at 1:11 AM
paperposterbot.bsky.social
arXiv📈🤖
Bridge-Anchored Partial Identification of a Target Mean under Outcome-Model Drift
By Lee, Jeon, Yang
Transporting an outcome relationship from a source population to a target population where the outcome is unobserved requires the conditional outcome law to be stable across populations. When it is not (outcome-model drift), the target mean is not point-identified and existing transportability estimators are biased. We study settings, common in cross-cohort educational and biomedical data integration, in which bridge outcomes are recorded in both populations and can be expected to drift alongside the target outcome. The bridge splits the drift into a component it can detect, fixed by a bridge-matching equation, and a residual orthogonal to the bridge that no observed quantity restricts. Because the target is a mean, the residual acts through a single scalar sensitivity parameter $\kappa$. We prove that the identified set has a closed-form irreducible core, whose width is governed by the residual bound and by how much of the outcome the bridge leaves unexplained, and that its center is first-order invariant to the residual. These results hold for any working exponential family, with point identification arising only as the $\kappa=0$ benchmark. We develop a debiased, drift-augmented estimator that is semiparametrically efficient at anchored sensitivity, doubly robust conditional on the bridge-identified drift, and yields rate-robust Imbens-Manski inference for the set. Unlike a bridge-blind sensitivity analysis, which must bound the entire drift channel, the proposed analysis bounds only its bridge-orthogonal part. Simulations confirm that the drift estimator is unbiased under co-drift, that the core width follows its closed form, and that the set fails visibly once the residual bound is exceeded. The motivating setting is the integration of two survey cohorts whose kindergarten mathematics outcome is available in only one.
September 30, 2026 at 8:40 PM
paperposterbot.bsky.social
arXiv📈🤖
Bridge-Anchored Partial Identification of a Target Mean under Outcome-Model Drift
By Lee, Jeon, Yang
Transporting an outcome relationship from a source population to a target population where the outcome is unobserved requires the conditional outcome law to be stable across populations. When it is not (outcome-model drift), the target mean is not point-identified and existing transportability estimators are biased. We study settings, common in cross-cohort educational and biomedical data integration, in which bridge outcomes are recorded in both populations and can be expected to drift alongside the target outcome. The bridge splits the drift into a component it can detect, fixed by a bridge-matching equation, and a residual orthogonal to the bridge that no observed quantity restricts. Because the target is a mean, the residual acts through a single scalar sensitivity parameter $\kappa$. We prove that the identified set has a closed-form irreducible core, whose width is governed by the residual bound and by how much of the outcome the bridge leaves unexplained, and that its center is first-order invariant to the residual. These results hold for any working exponential family, with point identification arising only as the $\kappa=0$ benchmark. We develop a debiased, drift-augmented estimator that is semiparametrically efficient at anchored sensitivity, doubly robust conditional on the bridge-identified drift, and yields rate-robust Imbens-Manski inference for the set. Unlike a bridge-blind sensitivity analysis, which must bound the entire drift channel, the proposed analysis bounds only its bridge-orthogonal part. Simulations confirm that the drift estimator is unbiased under co-drift, that the core width follows its closed form, and that the set fails visibly once the residual bound is exceeded. The motivating setting is the integration of two survey cohorts whose kindergarten mathematics outcome is available in only one.
September 30, 2026 at 9:27 AM
paperposterbot.bsky.social
arXiv📈🤖
Does your cost-effectiveness model answer the question of interest? Marginal versus conditional inputs and transportability across populations
By Jansen, Campbell, Cope et al
Objectives: There has been increased appreciation of the differences between marginal and conditional estimates and different types of effect measures regarding their applicability to different target populations. This issue of transportability is of concern in model-based cost-effectiveness analysis (CEA) when treatment effects from (international) trials are applied to (country-specific) baseline risk estimates. The objective of this paper is to create awareness regarding the issues that arise when using different types of treatment effect and baseline risk estimates in a model-based CEA to inform health technology assessment (HTA). Methods: We clarify collapsibility, marginal versus conditional estimation, and transportability; derive the ideal modeling approach implied by a marginal cost-effectiveness estimand; and characterize the issues of common modeling approaches, illustrated with a fictitious state-transition model. Results: An individual-level simulation that predicts outcomes from conditional inputs and averages them over the target population targets the marginal cost-effectiveness estimand. Cohort-model approaches that marginalize inputs early, evaluate an outcome regression model at mean covariates, or combine a conditional effect with a marginal baseline (or vice versa) can misstate cost-effectiveness results even with correct-population inputs; inputs from the wrong population add further error. Conclusions: The most rigorous approach is an individual-level simulation that carries conditional inputs (baseline risk, treatment effect, prognostic effects and effect modifiers) and marginalizes late. Cohort approaches instead marginalize early, relying on aggregated inputs, and do not necessarily target the marginal cost-effectiveness estimand of interest for HTA. Model developers should document, for each input, whether it is marginal or conditional and its population.
September 29, 2026 at 11:31 AM
arxiv-daily-bot.bsky.social
What Would Falsify It? A Variable Specific Evidence Standard for Mechanistic Claims About Self Explanation

Arshia Eftekhari zadeh

#arXiv #cs.AI #cs.LG
What Would Falsify It? A Variable Specific Evidence Standard for Mechanistic Claims About Self Explanation
When a language model explains an answer it has already given, does it reuse the computation that produced the answer or reconstruct a story from the answer alone? Attribution, transportability and recoverability are each compatible with causal use without establishing it. We propose an evidence st…
arxiv.org
September 29, 2026 at 7:05 PM
statme-bot.bsky.social
Jansen, Campbell, Cope, Phillippo, Remiro-Az\'ocar: Does your cost-effectiveness model answer the question of interest? Marginal versus conditional inputs and transportability across populations https://arxiv.org/abs/2609.35680 https://arxiv.org/pdf/2609.35680 https://arxiv.org/html/2609.35680
September 29, 2026 at 7:03 AM
biorxiv-bioinfo.bsky.social
External Evaluation of Multi-Omics Prognostic Models Across TCGA-BRCA and METABRIC: Transportability, Stability, and Incremental Performance https://www.biorxiv.org/content/10.64898/2026.09.18.752709v1
September 25, 2026 at 2:46 AM